CN115933398A - Air compression system linkage control energy-saving method and system based on Internet of things - Google Patents
Air compression system linkage control energy-saving method and system based on Internet of things Download PDFInfo
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- CN115933398A CN115933398A CN202211607930.4A CN202211607930A CN115933398A CN 115933398 A CN115933398 A CN 115933398A CN 202211607930 A CN202211607930 A CN 202211607930A CN 115933398 A CN115933398 A CN 115933398A
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Abstract
The invention discloses an air compression system linkage control energy-saving method and system based on the Internet of things, wherein the method comprises the following steps of: s1, establishing a compressed air preparation system operation monitoring platform based on an Internet of things method, wherein the operation monitoring platform is electrically connected with each device in an Internet of things platform system; s2, collecting energy consumption data of each device of the system through the Internet of things technology; s3, establishing an AI algorithm model of the compressed air group control system based on the platform system of the Internet of things; s4, establishing an efficiency curve model for each device in the system through an AI algorithm model based on the data acquired in the S2, and automatically optimizing in the operation process; the operation monitoring platform comprises a control system function module, an energy management module, a system health management module and the like, and the method and the system provided by the invention can accurately fit and match a compressed air supply curve with a terminal demand, ensure that the system stably supplies production demands, and reduce the problem of energy waste caused by excessive output.
Description
Technical Field
The invention relates to the technical field of energy conservation, in particular to an air compression system linkage control energy-saving method and system based on the Internet of things.
Background
In recent years, with the vigorous development of the automobile industry in China, the energy consumption of industrial production is continuously increased in proportion to the total energy consumption in cities, and the call for building energy-saving green factories is increasingly strong. Especially, the energy cost of automobile manufacturing factories in China is high, how to improve energy management and control, realize saving and reducing cost and improve the product competitiveness of the automobile manufacturing factories is of great industrial attention. The traditional energy-saving control mode is relatively lagged behind, and the current situation is as follows:
1. traditional air compression systems in the market stay in data collection and simple statistical algorithms, and have certain limitations, such as: the parameters used by the compressed air cannot be obtained, namely the use requirement of the terminal compressed air cannot be obtained, so that the supply curve of the compressed air and the use requirement of the terminal compressed air cannot be accurately matched, and when the system supplies and produces, the supply of the compressed air and the requirement of the terminal equipment cannot be accurately matched, and meanwhile, the running state of each piece of equipment cannot be accurately predicted and adjusted in advance, so that the waste of resources is caused.
2. When the production and marketing of a factory are adjusted, or the outdoor environment and other working conditions are changed, the usage amount of compressed air cannot be accurately predicted in a workshop, the compressed air pre-actual difference is large, and compressed air KPI which cannot be substantially controlled by an energy department is used. The algorithm is dedicated to research on high-level deep combination of information digitization and industrialization, a low-cost mature internet platform data system is independently researched and developed, and the efficiency change condition of equipment is monitored through equipment data informatization point inspection collection to ensure efficient operation; analyzing a production compressed air demand curve and an equipment energy supply curve, remotely and automatically controlling a system, and constructing an equipment management and control system combining automation and remote control; and establishing a compressed air model with the time unit divided by the minute-level precision, and searching a compressed air loss point through the model to provide guidance for energy conservation improvement.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention aims to provide an air compression system linkage control energy-saving method and system based on the Internet of things, so as to solve the problems in the background technology.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: an air compression system linkage control energy-saving method based on the Internet of things comprises the following steps:
s1, establishing a compressed air preparation system operation monitoring platform based on an Internet of things method, wherein the operation monitoring platform is electrically connected with each device in an Internet of things platform system;
s2, acquiring energy consumption data of each device of the system through the technology of the Internet of things;
s3, establishing an AI algorithm model of the compressed air group control system based on the platform system of the Internet of things;
s4, establishing an efficiency curve model for each device in the system through an AI algorithm model based on the data acquired in the S2, and automatically optimizing the efficiency curve model in the operation process;
and S5, monitoring each parameter used by the compressed air by using an output end, and accurately predicting the use requirement of the compressed air at the tail end by using an AI algorithm.
And S6, accurately predicting and pre-adjusting the running state (adding and subtracting the unit and adjusting the frequency of the frequency converter) of each device according to the prediction requirement and by combining the energy conversion efficiency and the energy conversion capacity of each device in the system.
Preferably, the energy consumption data in S2 includes energy data for main system devices and auxiliary devices such as an air compressor, a refrigeration dryer, a cooling water pump, and a cooling tower, and energy consumption conversion data (flow rate, pressure, load factor, etc.).
Preferably, the AI algorithm model in S3 comprises two groups of algorithms, wherein one group of algorithms is established according to the predicted future five-minute compressed air demand, and the other group of algorithms is established according to the compressed air flow demand matching related unit loads.
Preferably, the predicted future five minute compressed air algorithm formula is:
L 0 ＝f(P go out 、P Into 、ΔP 5 minutes 、ΔL 5 minutes 、L T 、L T0 、ΔL Tl )
Wherein L is 0 = predicted flow demand, P Go out = outlet pressure, P Go into = inlet pressure, [ delta ] P 5 minutes = variance of pressure fluctuation in the last 5 minutes,. DELTA.L 5 minutes = variance of flow fluctuation in last 5 minutes, L T = same period historical flow demand, L T0 = current flow, Δ LT1 = last predicted demand and actual demand difference;
P go out 、P Into For calculating the pressure loss of the pipeline and the pressure difference to be adjusted, delta P5 minutes And Δ L5 minutes For measuring and calculating the amplitude of variation of demand, L T And L T0 And Δ LT1 The system is used for correcting flow adjustment errors, flow requirements and fluctuation amplitudes of the next five minutes are obtained through linear regression algorithm and linear discriminant calculation every five minutes, the flow requirements and fluctuation amplitudes are used for adjusting unit load and pre-adding and subtracting a machine, the system continuously improves the weight of each parameter in an adjustment function in actual use, a calculation model is gradually optimized, and finally stable adjustment and output are achieved.
Preferably, the algorithm formula for matching the compressed air flow demand with the related unit load is as follows:
a decision tree model is adopted for dynamic adjustment of the air compressor unit, a plurality of decision tree classifiers are established according to different requirements, the classifiers are established according to a certain step length based on actual using requirements, the load state of the air compressor unit is dynamically adjusted in a random-like forest mode according to the current demand, accordingly, the optimal energy conversion output is obtained, the optimal energy saving output is obtained, the decision tree is established in a perfect mode continuously according to actual system operation output data in the operation of the system, the decision tree is established in an optimized mode, and the output conversion efficiency of the single unit under different loads is based on.
The utility model provides an air compressor system coordinated control economizer system based on thing networking, includes operation monitoring platform, operation monitoring platform includes and comprises control system function module, energy management module and system health management module etc..
Preferably, the control system function module mainly relates to a quality control function and an energy-saving function; the energy management module relates to an energy consumption metering function, an energy consumption counting function, an energy efficiency analysis function and a report generation function; the system health management module relates to fault diagnosis, early warning and alarming, health management and cloud operation and maintenance.
Preferably, the operation monitoring platform further comprises an energy-saving algorithm module based on an air compressor unit optimization sequence control technology, and the system can automatically adjust the starting-up number of the air compressors and the working efficiency of the frequency conversion unit according to the air consumption of a terminal pipe network and pressure fluctuation.
(III) advantageous effects
Compared with the prior art, the invention provides an air compression system linkage control energy-saving method and system based on the Internet of things, and the method and system have the following beneficial effects: the method and the system provided by the invention can accurately fit and match the compressed air supply curve with the terminal demand, reduce the problem of energy waste caused by excessive output while ensuring the stable supply production demand of the system, and simultaneously, the system operates by optimizing equipment with optimal efficiency, thereby maximizing the energy conversion efficiency of a compressed air system and greatly reducing the energy conversion loss; compared with the traditional PLC control system which introduces an AI algorithm, the system can continuously optimize demand prediction and control logic in the operation process, so that the deployment cost of the system is more economic than that of the traditional industrial Internet.
Detailed Description
In order to better understand the purpose, structure and function of the invention, the method and system for linkage control and energy saving of the air compression system based on the internet of things are further described in detail.
The invention comprises the following steps: an air compression system linkage control energy-saving method based on the Internet of things comprises the following steps:
s1, establishing a compressed air preparation system operation monitoring platform based on an Internet of things method, wherein the operation monitoring platform is electrically connected with each device in an Internet of things platform system;
s2, collecting energy consumption data of each device of the system through the Internet of things technology;
s3, establishing an AI algorithm model of the compressed air group control system based on the platform system of the Internet of things;
s4, establishing an efficiency curve model for each device in the system through an AI algorithm model based on the data acquired in the S2, and automatically optimizing the efficiency curve model in the operation process;
and S5, monitoring various parameters of the compressed air by using an output end, and accurately predicting the use requirement of the compressed air at the tail end by using an AI algorithm.
And S6, accurately predicting and pre-adjusting the running state of each device (adding and subtracting the set and adjusting the frequency of the frequency converter) according to the prediction requirement and by combining the energy conversion efficiency and the energy conversion capacity of each device in the system.
Specifically, the energy consumption data includes energy data for main system equipment and auxiliary equipment such as an air compressor, a refrigeration dryer, a cooling water pump and a cooling tower, and energy consumption conversion data (flow, pressure, load rate and the like), the acquired data is input to an operation monitoring platform, an efficiency curve model is established for each equipment in the system through an AI algorithm, and the data is automatically optimized in the operation process, it needs to be further explained that the AI algorithm model in S3 includes two groups of algorithms, one of which is established according to the demand of compressed air for predicting the next five minutes, and the other is established according to the demand of compressed air flow and the load of related units matched with the demand of the compressed air flow.
Specifically, the formula of the algorithm for predicting the future five-minute compressed air is as follows:
L 0 ＝f(P go out 、P Into 、ΔP 5 minutes 、ΔL 5 minutes 、L T 、L T0 、ΔL T1 ）
Wherein L is 0 = predicted flow demand, P Go out = outlet pressure, P Into = inlet pressure, [ delta ] P 5 minutes = variance of pressure fluctuation in the last 5 minutes,. DELTA.L 5 minutes = variance of flow fluctuation in last 5 minutes, L T = historical traffic demand in the same period, L T0 = current flow, Δ LT1 = last predicted demand and actual demand difference;
P go out 、P Into For calculating the pressure loss of the pipeline and the pressure difference required to be adjusted, delta P5 minutes And Δ L5 minutes For measuring and calculating the amplitude of variation of demand, L T And L T0 And Δ LT1 For correcting flow regulation errors, by linear regression algorithm and line every five minutesThe system continuously perfects the weight of each parameter in the adjusting function through actual use, gradually optimizes the calculation model, and finally tends to stably adjust output.
Specifically, the algorithm formula for matching the compressed air flow demand with the load of the relevant unit is as follows:
a decision tree model is adopted for dynamic adjustment of the air compressor unit, a plurality of decision tree classifiers are established according to different requirements, the classifiers are established according to a certain step length based on actual using requirements, the load state of the air compressor unit is dynamically adjusted in a random-like forest mode according to the current demand, accordingly, the optimal energy conversion output is obtained, the optimal energy saving output is obtained, the decision tree is established in a perfect mode continuously according to actual system operation output data in the operation of the system, the decision tree is established in an optimized mode, and the output conversion efficiency of the single unit under different loads is based on.
The method and the system can accurately fit and match the compressed air supply curve with the tail end requirement, ensure the stable supply production requirement of the system and reduce the energy waste problem caused by excessive output;
the invention relates to an air compression system linkage control energy-saving system based on the Internet of things, which comprises an operation monitoring platform, wherein the operation monitoring platform comprises a control system function module, an energy management module, a system health management module and the like, wherein the control system function module mainly relates to a quality control function and an energy-saving function; the energy management module relates to an energy consumption metering function, an energy consumption statistical function, an energy efficiency analysis function and a report generation function; the system health management module relates to fault diagnosis, early warning, health management and cloud operation and maintenance;
the system integrates data collection, data mining, data utilization, strategy control and user interaction, based on the AI energy-saving concept, the system thinking of the Internet of things and energy saving is applied, the air compression technology, the Internet of things technology, the automatic control technology, the computer communication technology and the big data analysis technology are fused, the production process parameters and the equipment performance parameter demand condition are combined, the compressed air system is subjected to real-time optimization control, the system is operated at the optimal energy efficiency state point, intelligent on-demand air supply is realized, on the premise of ensuring on-demand supply, the air quantity waste of the system is reduced to the maximum extent, and therefore the energy-saving new generation compressed air AI energy-saving system is realized;
the energy consumption metering module can monitor energy consumption parameters such as water, electricity and gas in the system in real time to generate corresponding curve graphs and reports. Real-time on-line monitoring is carried out on various devices such as an air compressor unit, a drying unit, a cooling water pump and the real-time power consumption, the accumulated energy consumption, three-phase voltage, three-phase current and three-phase power of a cooling tower;
the operation monitoring platform further comprises an energy-saving algorithm module based on an air compressor set optimization sequence control technology, the system can automatically adjust the starting number of the air compressors and the working efficiency of the frequency conversion set according to the gas consumption of the tail end pipe network and the pressure fluctuation, and the system can automatically adjust the starting number of the air compressors and the working frequency of the frequency conversion set according to the gas consumption of the tail end pipe network and the pressure fluctuation. In actual optimization sequence control, when one air compressor is loaded or unloaded, one air compressor is added or reduced correspondingly for the dryer, the cooling water pump and the cooling tower, so that the power consumption of the dryer, the water pump and the cooling tower is also considered when the efficient working area of the air compression system is considered. By means of an optimization algorithm, the total power consumption of the air compressor, the dryer, the water pump and the cooling tower is kept to be minimum while the requirement of the air consumption at the tail end of a site is met. Meanwhile, the actual running time of each air compressor is also considered in the actual optimization sequence control, so that the actual running time of each air compressor is consistent, the fatigue running of equipment is avoided, and the aim of saving energy is further fulfilled.
The whole system supports a double management and control framework of a cloud platform and a local platform, normal use and energy conservation of the whole system can be still realized under the condition of no public network, and the operation monitoring of the system is realized by adopting an Internet of things mode, so that the deployment cost of the system is more economic than that of the traditional industrial Internet.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. An air compression system linkage control energy-saving method based on the Internet of things is characterized by comprising the step of establishing an Internet of things platform system, and the energy-saving method comprises the following steps:
s1, establishing an operation monitoring platform of a compressed air preparation system based on an Internet of things method, wherein the operation monitoring platform is electrically connected with each device in the Internet of things platform system;
s2, acquiring energy consumption data of each device of the system through the technology of the Internet of things;
s3, establishing an AI algorithm model of the compressed air group control system based on the platform system of the Internet of things;
s4, establishing an efficiency curve model for each device in the system through an AI algorithm model based on the data acquired in the S2, and automatically optimizing in the operation process;
s5, monitoring various parameters of the compressed air by using an output end, and accurately predicting the use requirement of the compressed air at the tail end by using an AI algorithm;
and S6, accurately predicting and pre-adjusting the running state (adding and subtracting the unit and adjusting the frequency of the frequency converter) of each device according to the prediction requirement and by combining the energy conversion efficiency and the energy conversion capacity of each device in the system.
2. The air compression system linkage control energy-saving method based on the internet of things as claimed in claim 1, wherein the energy consumption data in S2 includes energy data for main system equipment and auxiliary equipment such as an air compressor, a refrigeration dryer, a cooling water pump and a cooling tower, and energy consumption conversion data (flow, pressure, load rate, etc.).
3. The air compression system linkage control energy-saving method based on the Internet of things according to claim 1, wherein the AI algorithm model in the S3 comprises two sets of algorithms, one set is established according to a predicted air compression requirement of five minutes in the future, and the other set is established according to a compressed air flow requirement matched with related unit loads.
4. The air compression system linkage control energy-saving method based on the Internet of things according to claim 3, wherein an algorithm formula for predicting the future five-minute compressed air is as follows:
L 0 ＝f(P go out 、P Go into 、ΔP 5 minutes 、ΔL 5 minutes 、L T 、L T0 、ΔL T1 )
Wherein L is 0 = predicted flow demand, P Go out = outlet pressure, P Into = inlet pressure, [ delta ] P 5 minutes = variance of pressure fluctuation in the last 5 minutes,. DELTA.L 5 minutes = variance of flow fluctuation in last 5 minutes, L T = same period historical flow demand, L T0 = current flow, Δ LT1 = last predicted demand and actual demand difference;
P go out 、P Into For calculating the pressure loss of the pipeline and the pressure difference to be adjusted, delta P5 minutes And Δ L5 minutes For measuring and calculating the amplitude of variation of demand, L T And L T0 And Δ LT1 The system is used for correcting flow adjustment errors, flow requirements and fluctuation amplitudes of the next five minutes are obtained through linear regression algorithm and linear discriminant calculation every five minutes, the flow requirements and fluctuation amplitudes are used for adjusting unit load and pre-adding and subtracting a machine, the system continuously improves the weight of each parameter in an adjustment function in actual use, a calculation model is gradually optimized, and finally stable adjustment and output are achieved.
5. The air compression system linkage control energy-saving method based on the Internet of things according to claim 3, wherein the algorithm formula for matching the compressed air flow demand with the load of the related unit is as follows:
the method comprises the steps of adopting a decision tree model for dynamic adjustment of the air compressor unit, establishing a plurality of decision tree classifiers according to different requirements, establishing the classifiers according to a certain step length based on actual use requirements, dynamically adjusting the load state of the air compressor unit in a random-like forest mode according to the current demand, obtaining the optimal energy conversion output, obtaining the optimal energy saving output, continuously and perfectly establishing the decision tree according to actual system operation output data during operation of the system, optimally establishing the decision tree, and based on the output conversion efficiency of a single unit under different loads.
6. The air compression system linkage control energy-saving system based on the Internet of things as claimed in any one of claims 1 to 5, comprising an operation monitoring platform, wherein the operation monitoring platform comprises a control system function module, an energy management module, a system health management module and the like.
7. The air compression system linkage control energy-saving system based on the Internet of things as claimed in claim 6, wherein the control system function module mainly relates to a quality control function and an energy-saving function; the energy management module relates to an energy consumption metering function, an energy consumption statistical function, an energy efficiency analysis function and a report generation function; the system health management module relates to fault diagnosis, early warning and alarming, health management and cloud operation and maintenance.
8. The air compressor system linkage control energy-saving system based on the internet of things as claimed in claim 6, wherein the operation monitoring platform further comprises an energy-saving algorithm module based on an air compressor unit optimization sequence control technology, and the system can automatically adjust the starting number of the air compressors and the working efficiency of the frequency conversion unit according to the air consumption of a terminal pipe network and pressure fluctuation.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578074A (en) * | 2023-07-14 | 2023-08-11 | 德耐尔能源装备有限公司 | Centralized monitoring control method and system for container nitrogen compressor unit |
| CN118361386A (en) * | 2024-06-19 | 2024-07-19 | 南京深度智控科技有限公司 | Air compression system energy-saving optimal control method based on tail end gas demand change |
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- 2022-12-14 CN CN202211607930.4A patent/CN115933398A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578074A (en) * | 2023-07-14 | 2023-08-11 | 德耐尔能源装备有限公司 | Centralized monitoring control method and system for container nitrogen compressor unit |
| CN116578074B (en) * | 2023-07-14 | 2023-09-26 | 德耐尔能源装备有限公司 | Centralized monitoring control method and system for container nitrogen compressor unit |
| CN118361386A (en) * | 2024-06-19 | 2024-07-19 | 南京深度智控科技有限公司 | Air compression system energy-saving optimal control method based on tail end gas demand change |
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